
import pandas as pd
import numpy as np
import requests
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values
import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
import folium # map rendering library
import pandas_read_xml as pdx

from minisom import MiniSom

from geopy.geocoders import Nominatim # convert an address into latitude and longitude values

! pip install folium==0.5.0
import folium # plotting library

# import k-means from clustering stage
from sklearn.cluster import KMeans

from sklearn import preprocessing, cluster
import scipy

import matplotlib.pyplot as plt
import seaborn as sns

print('Folium installed')
print('Libraries imported.')

Requirement already satisfied: folium==0.5.0 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (0.5.0)
Requirement already satisfied: branca in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from folium==0.5.0) (0.4.2)
Requirement already satisfied: requests in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from folium==0.5.0) (2.24.0)
Requirement already satisfied: jinja2 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from folium==0.5.0) (2.11.2)
Requirement already satisfied: six in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from folium==0.5.0) (1.15.0)
Requirement already satisfied: chardet<4,>=3.0.2 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from requests->folium==0.5.0) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from requests->folium==0.5.0) (2020.6.20)
Requirement already satisfied: idna<3,>=2.5 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from requests->folium==0.5.0) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from requests->folium==0.5.0) (1.25.11)
Requirement already satisfied: MarkupSafe>=0.23 in /Users/maxim/opt/anaconda3/lib/python3.8/site-packages (from jinja2->folium==0.5.0) (1.1.1)
Folium installed
Libraries imported.


CLIENT_ID = '...' #Key for working with the geocoder


prep_pools = pd.read_excel('Pools/pools.xlsx')


prep_pools.head()


prep_pools.drop(['global_id', 'PhotoWinter', 'Email', 'WebSite', 'HelpPhone', 'NameWinter', 
                 'HelpPhoneExtension', 'WorkingHoursWinter', 'ClarificationOfWorkingHoursWinter',
                 'HasEquipmentRental', 'EquipmentRentalComments', 'HasTechService', 'DisabilityFriendly',
                 'TechServiceComments', 'Lighting', 'SurfaceTypeWinter', 'geodata_center', 'geoarea',
                 'UsagePeriodWinter', 'DimensionsWinter', 'Seats', 'Paid', 'PaidComments', 'HasDressingRoom',
                 'HasEatery', 'HasToilet', 'HasWifi', 'HasCashMachine', 'HasFirstAidPost', 'HasMusic', 'ServicesWinter'], axis=1, inplace = True)


prep_pools.head()


missing_data = prep_pools.isnull()


for column in missing_data.columns.values.tolist():
    print(column)
    print (missing_data[column].value_counts())
    print("")

ObjectName
False    472
Name: ObjectName, dtype: int64

AdmArea
False    472
Name: AdmArea, dtype: int64

District
False    472
Name: District, dtype: int64

Address
False    472
Name: Address, dtype: int64


prep_pools['Category'] = 'Pools'


prep_pools['Longitude'] = ''


prep_pools['Latitude'] = ''


for index, row in prep_pools.iterrows():
    try:
        address = 'Москва,' + row['Address']
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        venue = results['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point']['pos']
        res = venue.split(sep=" ")
        prep_pools['Latitude'][index] = res[1]
        prep_pools['Longitude'][index] = res[0]  
    except:
        prep_pools['Latitude'][index] = 'Nan'
        prep_pools['Longitude'][index] = 'Nan'


prep_pools.head(10)


prep_pools.at[35, 'Longitude'] = 37.617959
prep_pools.at[35, 'Latitude'] = 55.898765

prep_pools.at[61, 'Longitude'] = 37.700828
prep_pools.at[61, 'Latitude'] = 55.793288

prep_pools.at[220, 'Longitude'] = 37.429986
prep_pools.at[220, 'Latitude'] = 55.658764

prep_pools.at[317, 'Longitude'] = 37.480849
prep_pools.at[317, 'Latitude'] = 55.621648

prep_pools.at[318, 'Longitude'] = 37.480849
prep_pools.at[318, 'Latitude'] = 55.621648


prep_pools["Longitude"] = pd.to_numeric(prep_pools["Longitude"])


prep_pools["Latitude"] = pd.to_numeric(prep_pools["Latitude"])


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_pools['Latitude'], prep_pools['Longitude'], prep_pools['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


prep_xml_adulthospitals = pdx.read_xml('AdultHospitals/adulthospital.xml', ['catalog'], encoding='windows-1251')


prep_xml_adulthospitals = pdx.flatten(prep_xml_adulthospitals)


prep_xml_adulthospitals.head()


prep_xml_adulthospitals = prep_xml_adulthospitals.reset_index()


prep_xml_adulthospitals.index

RangeIndex(start=0, stop=44, step=1)


data = []
for number in range(44):
    try:
        data.append([prep_xml_adulthospitals['array|ShortName'][number], 'None', 'None', prep_xml_adulthospitals['array|ObjectAddress'][number][0]['Address'], 'AdultHospital'])
    except KeyError:
        data.append([prep_xml_adulthospitals['array|ShortName'][number], 'None', 'None', prep_xml_adulthospitals['array|ObjectAddress'][number]['Address'], 'AdultHospital'])
prep_adulthospitals = pd.DataFrame(data, columns=['ObjectName', 'AdmArea', 'District', 'Address', 'Category'])


prep_adulthospitals.head()


prep_adulthospitals['Longitude'] = ''
prep_adulthospitals['Latitude'] = ''


for index, row in prep_adulthospitals.iterrows():
    try:
        address = row['Address']
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        venue = results['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point']['pos']
        res = venue.split(sep=" ")
        prep_adulthospitals['Latitude'][index] = res[1]
        prep_adulthospitals['Longitude'][index] = res[0]  
    except:
        prep_adulthospitals['Latitude'][index] = 'Nan'
        prep_adulthospitals['Longitude'][index] = 'Nan'


prep_adulthospitals.head()


prep_adulthospitals["Longitude"] = pd.to_numeric(prep_adulthospitals["Longitude"])


prep_adulthospitals["Latitude"] = pd.to_numeric(prep_adulthospitals["Latitude"])


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_adulthospitals['Latitude'], prep_adulthospitals['Longitude'], prep_adulthospitals['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


prep_xml_adultdentalcare = pdx.read_xml('AdultDentalСare/adultdentalcare.xml', ['catalog'], encoding='windows-1251')


prep_xml_adultdentalcare = pdx.flatten(prep_xml_adultdentalcare)


prep_xml_adultdentalcare.head()


prep_xml_adultdentalcare.index

RangeIndex(start=0, stop=67, step=1)


data = []
for number in range(67):
    try:
        data.append([prep_xml_adultdentalcare['array|ShortName'][number], 'None', 'None', prep_xml_adultdentalcare['array|ObjectAddress'][number][0]['Address'], 'AdultDentalClinic'])
    except KeyError:
        data.append([prep_xml_adultdentalcare['array|ShortName'][number], 'None', 'None', prep_xml_adultdentalcare['array|ObjectAddress'][number]['Address'], 'AdultDentalClinic'])
prep_adultdentalcare = pd.DataFrame(data, columns=['ObjectName', 'AdmArea', 'District', 'Address', 'Category'])


prep_adultdentalcare.head()


prep_adultdentalcare['Longitude'] = ''
prep_adultdentalcare['Latitude'] = ''


for index, row in prep_adultdentalcare.iterrows():
    try:
        address = row['Address']
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        venue = results['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point']['pos']
        res = venue.split(sep=" ")
        prep_adultdentalcare['Latitude'][index] = res[1]
        prep_adultdentalcare['Longitude'][index] = res[0]  
    except:
        prep_adultdentalcare['Latitude'][index] = 'Nan'
        prep_adultdentalcare['Longitude'][index] = 'Nan'


prep_adultdentalcare["Longitude"] = pd.to_numeric(prep_adultdentalcare["Longitude"])


prep_adultdentalcare["Latitude"] = pd.to_numeric(prep_adultdentalcare["Latitude"])


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_adultdentalcare['Latitude'], prep_adultdentalcare['Longitude'], prep_adultdentalcare['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


prep_xml_childrendentalcare = pdx.read_xml('ChildDentalCare/childdentalcare.xml', ['catalog'], encoding='windows-1251')


prep_xml_childrendentalcare = pdx.flatten(prep_xml_childrendentalcare)


prep_xml_childrendentalcare.head()


prep_xml_childrendentalcare.index

RangeIndex(start=0, stop=40, step=1)


data = []
for number in range(40):
    try:
        data.append([prep_xml_childrendentalcare['array|ShortName'][number], 'None', 'None', prep_xml_childrendentalcare['array|ObjectAddress'][number][0]['Address'], 'ChildrenDentalClinic'])
    except KeyError:
        data.append([prep_xml_childrendentalcare['array|ShortName'][number], 'None', 'None', prep_xml_childrendentalcare['array|ObjectAddress'][number]['Address'], 'ChildrenDentalClinic'])
prep_childrendentalcare = pd.DataFrame(data, columns=['ObjectName', 'AdmArea', 'District', 'Address', 'Category'])


prep_childrendentalcare.head()


prep_childrendentalcare['Longitude'] = ''
prep_childrendentalcare['Latitude'] = ''


for index, row in prep_childrendentalcare.iterrows():
    try:
        address = row['Address']
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        venue = results['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point']['pos']
        res = venue.split(sep=" ")
        prep_childrendentalcare['Latitude'][index] = res[1]
        prep_childrendentalcare['Longitude'][index] = res[0]
        
    except:
        prep_childrendentalcare['Latitude'][index] = 'Nan'
        prep_childrendentalcare['Longitude'][index] = 'Nan'


prep_childrendentalcare["Longitude"] = pd.to_numeric(prep_childrendentalcare["Longitude"])


prep_childrendentalcare["Latitude"] = pd.to_numeric(prep_childrendentalcare["Latitude"])


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_adultdentalcare['Latitude'], prep_adultdentalcare['Longitude'], prep_adultdentalcare['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


prep_xml_childrenhospital = pdx.read_xml('ChildrenHospitals/childrenhospital.xml', ['catalog'], encoding='windows-1251')


prep_xml_childrenhospital = pdx.flatten(prep_xml_childrenhospital)


prep_xml_childrenhospital.head()


prep_xml_childrenhospital.index

RangeIndex(start=0, stop=15, step=1)


data = []
for number in range(15):
    try:
        data.append([prep_xml_childrenhospital['array|ShortName'][number], 'None', 'None', prep_xml_childrenhospital['array|ObjectAddress'][number][0]['Address'], 'ChildrenHospital'])
    except KeyError:
        data.append([prep_xml_childrenhospital['array|ShortName'][number], 'None', 'None', prep_xml_childrenhospital['array|ObjectAddress'][number]['Address'], 'ChildrenHospital'])
prep_childrenhospital = pd.DataFrame(data, columns=['ObjectName', 'AdmArea', 'District', 'Address', 'Category'])


prep_childrenhospital.head()


prep_childrenhospital['Longitude'] = ''
prep_childrenhospital['Latitude'] = ''


for index, row in prep_childrenhospital.iterrows():
    try:
        address = row['Address']
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        venue = results['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point']['pos']
        res = venue.split(sep=" ")
        prep_childrenhospital['Latitude'][index] = res[1]
        prep_childrenhospital['Longitude'][index] = res[0] 
    except:
        prep_childrenhospital['Latitude'][index] = 'Nan'
        prep_childrenhospital['Longitude'][index] = 'Nan'


prep_childrenhospital["Longitude"] = pd.to_numeric(prep_childrenhospital["Longitude"])


prep_childrenhospital["Latitude"] = pd.to_numeric(prep_childrenhospital["Latitude"])


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_childrenhospital['Latitude'], prep_childrenhospital['Longitude'], prep_childrenhospital['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


prep_school = pd.read_csv('EducationData/school_data.csv')


prep_school.head()


prep_school['Category'] = 'School'
prep_school['AdmArea'] = ''
prep_school['District'] = ''
prep_school.rename(columns={"name": "ObjectName", "adress": "Address", "lattitude": "Latitude", "longitude": "Longitude"}, inplace = True)
prep_school = prep_school[['ObjectName', 'AdmArea', 'District', 'Address', 'Category', 'Longitude', 'Latitude']]
prep_school.head()


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(prep_school['Latitude'], prep_school['Longitude'], prep_school['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow


objects = pd.concat([prep_pools, prep_adulthospitals, prep_adultdentalcare, prep_childrendentalcare, prep_childrenhospital, prep_school], axis=0)


objects.reset_index(inplace = True)


objects.to_csv(r'objects.csv')


for index, row in objects.iterrows():
    try:
        long = str(objects['Longitude'].iloc[index])
        lat = str(objects['Latitude'].iloc[index])
        address = long + ',' + lat
        print(index, address)
        url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode={}'.format(CLIENT_ID, address)
        results = requests.get(url).json()
        objects.at[index, 'AdmArea'] = results['response']['GeoObjectCollection']['featureMember'][2]['GeoObject']['metaDataProperty']['GeocoderMetaData']['Address']['Components'][4]['name']
        objects.at[index, 'District'] = results['response']['GeoObjectCollection']['featureMember'][2]['GeoObject']['metaDataProperty']['GeocoderMetaData']['Address']['Components'][5]['name']
    except:
        print('error')

0 37.578298,55.879303
1 37.615632,55.730978
2 37.696642,55.771753000000004
3 37.60461,55.79014
4 37.61786,55.697138
5 37.585440000000006,55.684726
6 37.571525,55.906749
7 37.427094,55.865384
8 37.366583,55.831433
error
9 37.853093,55.72973100000001
10 37.853093,55.72973100000001
11 37.829799,55.759775
12 37.826017,55.746821
13 37.83359,55.724915
14 37.83359,55.724915
15 37.898008000000004,55.723055
16 37.567464,55.901833999999994
17 37.668597,55.785468
18 37.668597,55.785468
19 37.781524,55.70071
20 37.849571000000005,55.691902
21 37.737893,55.73186
22 37.726071000000005,55.649097999999995
23 37.56017,55.783590000000004
24 37.310375,55.613489
error
25 37.356468,55.832246999999995
26 37.356468,55.832246999999995
27 37.489697,55.882414000000004
28 37.563871,55.86228199999999
29 37.535061999999996,55.887251
30 37.469782,55.878586
31 37.447656,55.791430000000005
32 37.403073,55.646756
33 37.476869,55.67499599999999
34 37.519036,55.60479300000001
35 37.617959,55.898765
36 37.548536999999996,55.550259
37 37.548536999999996,55.550259
38 37.498833000000005,55.646731
39 37.498833000000005,55.646731
40 37.653756,55.883635999999996
41 37.803631,55.80616
42 37.529376,55.680448
43 37.579538,55.78804399999999
44 37.633796000000004,55.83586700000001
45 37.581271,55.782552
46 37.71459,55.754298999999996
47 37.226087,55.986934
error
48 37.483409,55.640828000000006
49 37.849571000000005,55.691902
50 37.737893,55.73186
51 37.683446,55.70129300000001
52 37.353566,55.645461
53 37.411355,55.804481
54 37.731757,55.706766
55 37.487038,55.630878
56 37.581271,55.782552
57 37.565713,55.751406
58 37.457546,55.760408
59 37.347377,55.651663
60 37.509613,55.717284
61 37.700828,55.793288
62 37.501806,55.71334399999999
63 37.438933,55.72828199999999
64 37.558454,55.713069999999995
error
65 37.199595,55.973214
error
66 37.476276,55.844779
67 37.511435999999996,55.531698
68 37.584119,55.765838
69 37.564689,55.835604000000004
70 37.519324,55.876293999999994
71 37.730706,55.788706999999995
72 37.561733000000004,55.782956999999996
73 37.52068,55.798485
74 37.616521,55.629567
75 37.605032,55.640777
76 37.491099,55.851293000000005
77 37.617366,55.696189000000004
78 37.492913,55.61874
79 37.483957000000004,55.832094999999995
80 37.674184000000004,55.58408000000001
81 37.673286,55.628505000000004
82 37.551914000000004,55.843858999999995
83 37.479277,55.837267000000004
84 37.564383,55.847719999999995
85 37.667087,55.875713
86 37.497468,55.823216
87 37.677607,55.870819
88 37.574292,55.814103
89 37.649615000000004,55.707218000000005
90 37.791226,55.704240000000006
91 37.632179,55.866980000000005
92 37.617689,55.820181999999996
93 37.353566,55.645461
94 37.923709,55.685508
95 37.791226,55.704240000000006
96 37.633814,55.670524
97 37.366817,55.84307
98 37.35954,55.850747
99 37.411355,55.804481
100 37.62802,55.719308
101 37.565228000000005,55.885575
102 37.569063,55.852444999999996
103 37.350978999999995,55.598253
104 37.577211,55.611169999999994
105 37.538916,55.807891000000005
106 37.373571999999996,55.592113
107 37.650478,55.581688
108 37.65125,55.811002
109 37.58977,55.889058
110 37.611338,55.635040000000004
111 37.577328,55.798170999999996
112 37.58853,55.807193000000005
113 37.695411,55.65554399999999
error
114 37.483409,55.640828000000006
115 37.457744,55.735251
116 37.516763,55.793971
117 37.66556,55.879541
error
118 37.565228000000005,55.885575
119 37.618426,55.80445
120 37.478226,55.827403000000004
121 37.667204,55.856159
122 37.608401,55.85720500000001
123 37.226087,55.986934
error
124 37.540344,55.79506899999999
125 37.647037,55.822276
126 37.730706,55.788706999999995
127 37.923709,55.685508
128 37.595492,55.635975
129 37.695411,55.65554399999999
error
130 37.570141,55.859867
131 37.501241,55.858054
132 37.730706,55.788706999999995
133 37.534712,55.83221700000001
134 37.54356,55.799572999999995
135 37.611958,55.795914
136 37.7759,55.824238
137 37.29567,55.47152
138 37.29567,55.47152
139 37.852769,55.735443999999994
140 37.586311,55.751229
141 37.555723,55.759409999999995
142 37.555723,55.759409999999995
143 37.771696,55.747296999999996
144 37.742914,55.69175500000001
error
145 37.569719,55.565076
146 37.569719,55.565076
147 37.456271,55.667615000000005
148 37.555822,55.69939599999999
149 37.814115,55.752582
150 37.422863,55.843833
151 37.598133000000004,55.71795899999999
152 37.776745,55.824324
153 37.412756,55.86228199999999
154 37.584685,55.570471999999995
155 37.46008,55.842014
156 37.667959,55.823712
157 37.382834,55.903978
158 37.667959,55.823712
159 37.465011,55.668935
160 37.299703,55.490136
161 37.65602,55.745909
162 37.682179,55.760697
163 37.586311,55.773986
164 37.613341,55.721523
165 37.624049,55.785123999999996
166 37.653936,55.721914
167 37.693723,55.814558
168 37.753928,55.636199
169 37.391628000000004,55.804627
170 37.642806,55.724266
171 37.700568,55.880904
172 37.585583,55.858129000000005
173 37.669477,55.886302
174 37.609002000000004,55.798642
175 37.473715999999996,55.582345
176 37.469737,55.579892
177 37.444521,55.53078000000001
178 37.369403999999996,55.58955400000001
179 37.660844,55.825967000000006
180 37.592878000000006,55.710296
181 37.579538,55.78804399999999
182 37.661922,55.682651
183 37.691207,55.739808
184 37.465532,55.781504000000005
185 37.703595,55.782861
186 37.649319,55.824562
187 37.584685,55.570471999999995
188 37.730247999999996,55.614775
189 37.709272,55.604335999999996
190 37.402983,55.721138
191 37.402983,55.721138
192 37.552193,55.79303
193 37.473051,55.67600600000001
194 37.473051,55.67600600000001
195 37.473051,55.67600600000001
196 37.770232,55.75055
197 37.493542,55.684045999999995
198 37.193217,55.30905799999999
199 37.88978,55.718030000000006
200 37.331288,55.501631
201 37.653343,55.70211
202 37.653343,55.70211
203 37.511266,55.447979000000004
204 37.579744,55.690654
205 37.632179,55.866980000000005
206 37.592509,55.776931999999995
error
207 37.588898,55.760692000000006
208 37.597315,55.762145
209 37.556101,55.815518999999995
210 37.530822,55.702952
211 37.483104,55.651185999999996
212 37.483104,55.651185999999996
213 37.411014,55.738595999999994
214 37.605777,55.773357999999995
215 37.516449,55.681712
216 37.688584000000006,55.707816
217 37.539087,55.747115
218 37.543255,55.54785
219 37.508229,55.73925500000001
220 37.429986,55.658764
221 37.596552,55.646980000000006
error
222 37.576861,55.664126
223 37.596552,55.646980000000006
error
224 37.527184000000005,55.597246
225 37.440829,55.739017000000004
226 37.549444,55.721392
error
227 37.497656,55.657408
228 37.497656,55.657408
229 37.530165999999994,55.64473
230 37.481648,55.634221999999994
231 37.498841999999996,55.74899
232 37.67687,55.758134
233 37.819657,55.678402
234 37.730437,55.729873
235 37.747738,55.681163
236 37.679565000000004,55.730872
237 37.61715,55.736523
238 37.621471,55.759912
239 37.626096999999994,55.74823000000001
error
240 37.568065999999995,55.767154000000005
241 37.510601,55.64755400000001
242 37.609586,55.754568000000006
243 37.532646,55.751801
244 37.658536,55.76169
245 37.613448999999996,55.738252
246 37.685907,55.7747
247 37.569134999999996,55.76324
248 37.610988,55.766698999999996
249 37.448465,55.807460999999996
250 37.549004,55.663745
251 37.536526,55.664095999999994
252 37.542770000000004,55.669945
253 37.559523,55.672321
254 37.656191,55.74335
255 37.489868,55.77038
256 37.575828,55.73676700000001
257 37.813423,55.730416000000005
258 37.64772,55.721138
259 37.607529,55.731981999999995
260 37.632826,55.711123
261 37.605912,55.754993000000006
262 37.576879,55.7249
263 37.449011999999996,55.790312
264 37.69401,55.79231600000001
265 37.568478999999996,55.859443000000006
266 37.77105,55.758635
267 37.712488,55.749263
268 37.476805999999996,55.78749200000001
269 37.762965,55.652207
270 37.591162,55.831555
271 37.899113,55.717290000000006
272 37.442895,55.752891000000005
273 37.852724,55.730005000000006
274 37.546974,55.75811899999999
275 37.674490000000006,55.63304300000001
276 37.77105,55.758635
277 37.579852,55.643190000000004
278 37.618749,55.714313
279 37.649741,55.674367000000004
280 37.428019,55.783428
error
281 37.555651,55.936439
282 37.766217,55.652212
283 37.64383,55.761816
284 37.504052,55.74149
285 37.694262,55.81618100000001
286 37.724175,55.880162
287 37.442895,55.752891000000005
288 37.695169,55.853678
289 37.455434999999994,55.735941000000004
290 37.491503,55.705346
291 37.568956,55.743770999999995
292 37.534406,55.750028
293 37.574067,55.730431
294 37.412137,55.723263
295 37.695169,55.853678
296 37.506612,55.715180000000004
297 37.647109,55.762161
298 37.631757,55.678362
299 37.701304,55.634308
300 37.505165999999996,55.737527
301 37.629996000000006,55.728565
302 37.41979,55.75975
303 37.665911,55.781615
304 37.628190000000004,55.730639000000004
305 37.193047,56.01568100000001
error
306 37.480885,55.727231999999994
307 37.496731,55.672428000000004
308 37.192499,55.98071899999999
error
309 37.484963,55.672189
310 37.457546,55.760408
311 37.48614,55.766572
312 37.802931,55.709830000000004
313 37.40592,55.754
314 37.76468,55.673747
315 37.500136,55.776531999999996
316 37.648789,55.729147999999995
317 37.480849,55.621648
318 37.480849,55.621648
319 37.521821,55.544484
320 37.665911,55.781615
321 37.649291999999996,55.88555899999999
322 37.424129,55.748630000000006
323 37.447656,55.791430000000005
324 37.584281,55.892526000000004
325 37.690614000000004,55.778902
326 37.624543,55.757957
327 37.505175,55.857488000000004
328 37.665911,55.781615
329 37.440829,55.739017000000004
330 37.57563,55.729524
331 37.438933,55.72828199999999
332 37.491170000000004,55.735519999999994
333 37.731227000000004,55.683446999999994
334 37.700568,55.880904
335 37.415874,55.839689
336 37.647037,55.822276
337 37.561949,55.689563
338 37.638108,55.800104000000005
error
339 37.699705,55.87759200000001
340 37.491503,55.705346
341 37.505165999999996,55.737527
342 37.701304,55.634308
343 37.556388,55.754203000000004
344 37.503370000000004,55.860928
345 37.687587,55.735606000000004
346 37.730706,55.788706999999995
347 37.803631,55.80616
348 37.591162,55.831555
349 37.730706,55.788706999999995
350 37.691207,55.739808
351 37.646381,55.70315
352 37.563871,55.86228199999999
353 37.613099,55.888881000000005
354 37.618516,55.739727
355 37.78739,55.789143
356 37.866334,55.82012099999999
error
357 37.618354,55.818881999999995
358 37.516763,55.793971
359 37.357851000000004,55.834233999999995
360 37.701403000000006,55.876324
361 37.70735,55.615802
362 37.478226,55.827403000000004
363 37.858204,55.681965000000005
364 37.670761999999996,55.87339
365 37.822801,55.757977000000004
366 37.710144,55.876304000000005
367 37.546974,55.75811899999999
368 37.641333,55.87891
369 37.360717,55.840614
370 37.604843,55.618491000000006
371 37.157697999999996,55.979923
error
372 37.420653,55.749683999999995
373 37.715812,55.617342
374 37.339975,55.636880000000005
375 37.686437,55.731759
376 37.465901,55.81995500000001
377 37.580957,55.675357
378 37.681281,55.81059200000001
error
379 37.599328,55.736686
380 37.383767999999996,55.839158
381 37.533418,55.726791000000006
382 37.428297,55.721047
383 37.670142,55.714043999999994
384 37.591602,55.668615
385 37.71583,55.680707
386 37.507403000000004,55.756928
387 37.43932,55.718571999999995
388 37.445707,55.725473
389 37.509352,55.661333
390 37.548823999999996,55.760899
391 37.548823999999996,55.760899
392 37.398383,55.617321999999994
393 37.188384,55.99784399999999
error
394 37.548823999999996,55.760899
395 37.574372,55.512561
error
396 37.447926,55.818392
397 37.834875,55.742473
398 37.516449,55.681712
399 37.717735,55.761219
400 37.856704,55.73358399999999
401 37.558706,55.677204
402 37.339831,55.647391000000006
403 37.357789000000004,55.595689
404 37.337441999999996,55.646289
405 37.581882,55.663771
406 37.339975,55.636880000000005
407 37.579035,55.50240600000001
408 37.698421,55.616417000000006
409 37.809191999999996,55.726836999999996
410 37.491512,55.674951
411 37.438888,55.564216
412 37.484253,55.79891
413 37.647558000000004,55.812757
414 37.445707,55.725473
415 37.587757,55.870757999999995
416 37.787794,55.79166800000001
417 37.294691,55.428383
418 37.713962,55.621409
419 37.404410999999996,55.83826800000001
420 37.675109,55.710692
421 37.410097,55.570131
422 37.677463,55.723486
423 37.211721999999995,55.30963199999999
424 37.410097,55.570131
425 37.581092,55.609512
426 37.421496999999995,55.74149
427 37.557897,55.796886
428 37.445644,55.524702000000005
429 37.720304,55.786758999999996
430 37.294691,55.428383
431 37.647558000000004,55.812757
432 37.613737,55.650206000000004
error
433 37.536454,55.943658
434 37.58764,55.862863
435 37.357789000000004,55.595689
436 37.832692,55.818447
437 37.454636,55.589197999999996
438 37.438888,55.564216
439 37.818849,55.82503199999999
440 37.720304,55.786758999999996
441 37.523061,55.83030600000001
442 37.646417,55.712731000000005
443 37.589707000000004,55.570299
444 37.711346999999996,55.602205000000005
445 37.868992999999996,55.816782999999994
error
446 37.904665,55.719986999999996
447 37.570537,55.729676
448 37.504483,55.860827
449 37.163213,55.977622
error
450 37.499695,55.703753000000006
451 37.402983,55.721138
452 37.704412,55.75836700000001
453 37.470464,55.829632999999994
454 37.390146,55.883858
455 37.645178,55.875173
456 37.449011999999996,55.790312
457 37.470464,55.829632999999994
458 37.702562,55.793206999999995
459 37.832018,55.812438
460 37.688755,55.73699499999999
461 37.543928,55.78636899999999
462 37.577903000000006,55.726076
463 37.569782000000004,55.764065
464 37.601915000000005,55.77539300000001
465 37.599928999999996,55.768733999999995
466 37.671462,55.77385400000001
467 37.681146000000005,55.787447
468 37.566728000000005,55.787376
469 37.498213,55.868304
470 37.498213,55.868304
471 37.552803999999995,55.937109
472 37.578945000000004,55.798318
473 37.270948,55.636396999999995
474 37.501034000000004,55.669691
475 37.598645,55.76704300000001
476 37.185357,55.313629000000006
477 37.598690000000005,55.719663
478 37.559056,55.688258
479 37.453971,55.815321999999995
480 37.665875,55.864763
481 37.814438,55.756280000000004
482 37.190253000000006,55.413540000000005
483 37.555409000000004,55.812721999999994
484 37.415371,55.642824
485 37.652625,55.616514
486 37.739329999999995,55.790701
487 37.685251,55.79226
488 37.739366,55.77276
489 37.832297,55.729958999999994
490 37.702265999999995,55.767782
491 37.48278,55.743451
492 37.603451,55.724433999999995
493 37.487811,55.803312
494 37.549669,55.781575
495 37.467105,55.771985
496 37.53437,55.88977
497 37.433948,55.722795999999995
498 37.749831,55.693256999999996
499 37.648663,55.746568
500 37.235699,55.983332999999995
error
501 37.809084000000006,55.799057
502 37.77997,55.801966
503 37.654781,55.672702
504 37.375665000000005,55.883848
error
505 37.630086,55.724353
506 37.599866999999996,55.694809
507 37.571282000000004,55.505021
508 37.628586,55.714642000000005
509 37.605382,55.697361
510 37.700523,55.798662
511 37.700083,55.792922999999995
512 36.947447,56.186504000000006
error
513 37.630131,55.247364000000005
error
514 37.655338,55.825906999999994
515 37.667025,55.71964200000001
516 37.570258,55.702946999999995
517 37.607206,55.654532999999994
error
518 37.566189,55.50055
error
519 37.580858,55.73188100000001
520 37.629313,55.725889
521 37.782638,55.752764
522 37.599759000000006,55.78754300000001
523 37.650163,55.765443000000005
524 37.512595000000005,55.874748
525 37.741611999999996,55.731004000000006
526 37.339957,55.6388
527 37.665524,55.618329
528 37.86372,55.739183999999995
529 37.67484,55.728287
530 37.720915000000005,55.744941000000004
531 37.300018,55.472689
532 37.652867,55.86910699999999
533 37.664402,55.78155
534 37.779385999999995,55.749547
535 37.581289,55.795727
536 37.715534000000005,55.756452
537 37.566144,55.864773
538 37.515415999999995,55.674372
539 37.640228,55.75644200000001
error
540 37.607143,55.65388299999999
error
541 37.452758,55.831817
542 37.524184000000005,55.859716000000006
543 37.640192,55.809565
544 37.747639,55.694672
545 37.794406,55.794810999999996
546 37.417284,55.771260999999996
547 37.563233000000004,55.745549
548 37.516368,55.822053000000004
549 37.579133,55.5687
550 37.717859999999995,55.649809
551 37.562182,55.744622
552 37.159306,55.976589000000004
error
553 37.721678000000004,55.800985
554 37.525145,55.64509
555 37.67634,55.707283999999994
556 37.749059,55.69941600000001
557 37.406648,55.797847
558 37.567806,55.845293999999996
559 37.404178,55.648499
560 37.58491,55.899356000000004
561 37.35203,55.847744999999996
562 37.238744,55.98426
error
563 37.467105,55.783524
564 37.534712,55.54919
565 37.615165000000005,55.713938
566 37.71027,55.87244499999999
567 37.571704,55.789325
568 37.653604,55.590810999999995
569 37.848125,55.699177
570 37.510331,55.48603000000001
571 37.348895,55.32563100000001
572 37.22526,55.519075
573 37.275259999999996,55.431228000000004
574 37.190253000000006,55.413540000000005
575 37.185357,55.313629000000006
576 37.345822999999996,55.596807999999996
577 36.942003,55.464861
578 37.481072999999995,55.568394999999995
error
579 37.351348,55.519723
580 37.162351,55.534597
581 37.536553000000005,55.474596999999996
582 37.802949,55.63740799999999
583 37.796184000000004,55.75786
584 37.763719,55.652679000000006
585 37.517608,55.672940000000004
586 37.674319,55.727952
587 37.469,55.802831000000005
588 37.524471000000005,55.835806000000005
589 37.74375,55.672351
590 37.547306,55.799826
591 37.589015,55.815746999999995
592 37.572576,55.818158999999994
593 37.815408000000005,55.791552
594 37.406648,55.797847
595 37.441673,55.847942
596 37.581164,55.72056
597 37.684865,55.879318999999995
598 37.650954,55.679285
599 37.581325,55.719338
600 37.514571999999994,55.794047
601 37.706865,55.61281700000001
602 37.159306,55.976589000000004
error
603 37.629313,55.725889
604 37.467787,55.802604
605 37.775828999999995,55.702830000000006
606 37.715534000000005,55.756452
607 37.35203,55.847744999999996
608 37.294017,55.474990000000005
609 37.606343,55.772375
610 37.467787,55.802604
611 37.822666999999996,55.81835600000001
612 37.639446,55.763138
613 37.585745,55.900103
614 37.606811,55.866177
615 37.586976,55.616814
616 37.411463,55.733528
617 37.500791,55.623076
618 37.373141,55.591666000000004
619 37.162351,55.534597
620 37.544243,55.475817000000006
621 37.487703,55.540654
622 37.515541999999996,55.739189
623 37.6198,55.724824
624 37.467105,55.771985
625 37.815336,55.756579
626 37.450179999999996,55.400864
627 37.540003000000006,55.754633
628 37.642599,55.859083999999996
629 37.705490000000005,55.758564
630 37.584407,55.76511899999999
631 37.697424,55.786961
632 37.767268,55.682463
633 37.405795,55.853759
634 37.531909000000006,55.827631000000004
635 37.230345,55.984053
error
636 37.650478,55.632205000000006
637 37.50178,56.23542
638 37.588808,55.76029200000001
639 37.589033,55.749866000000004
640 37.602067,55.783848
641 37.604987,55.748615
642 37.564931,55.725803000000006
643 37.64268,55.78035
644 37.681263,55.736741
645 37.674704999999996,55.78246600000001
646 37.677975,55.769276
647 37.595760999999996,55.787705
648 37.596965000000004,55.779737
649 37.663243,55.752607
650 37.64374,55.763554000000006
651 37.596246,55.777969999999996
652 37.604592,55.754735
653 37.633589,55.76474399999999
654 37.604906,55.760631000000004
655 37.625935999999996,55.772542
656 37.562227,55.758590000000005
657 37.580912,55.751249
658 37.603505,55.741333
659 37.597163,55.754142
660 37.664823999999996,55.743795999999996
661 37.63729,55.787548
662 37.629215,55.729012
663 37.68465,55.735302000000004
664 37.651987,55.737613
665 37.627642,55.726051
666 37.602436,55.757941
667 37.687497,55.770993000000004
668 37.672594000000004,55.735125
669 37.649265,55.764941
670 37.555723,55.759409999999995
671 37.57943,55.722223
672 37.614734000000006,55.759653
673 37.635071999999994,55.724489
674 37.672594000000004,55.735125
675 37.606784000000005,55.783818000000004
676 37.646345000000004,55.743613
677 37.632089,55.732032999999994
678 37.605068,55.750803000000005
679 37.64268,55.78035
680 37.58226,55.772573
681 37.592599,55.73483100000001
682 37.621435,55.744170999999994
683 37.637596,55.745605000000005
error
684 37.594755,55.756756
685 37.607044,55.770031
686 37.593983,55.738758999999995
687 37.584451,55.76635400000001
688 37.655634,55.760373
689 37.687973,55.768851
690 37.590192,55.753493999999996
691 37.637919000000004,55.767914000000005
692 37.592213,55.786919999999995
693 37.563521,55.760165
694 37.672666,55.772998
695 37.637209999999996,55.743634
error
696 37.593938,55.74784399999999
697 37.686662,55.778877
698 37.637389,55.752293
699 37.547028000000005,55.763417000000004
700 37.571471,55.737015
701 37.559684999999995,55.815741
702 37.570132,55.74677
703 37.61963,55.697163
704 37.6698,55.784364000000004
705 37.671624,55.723268000000004
706 37.652445,55.883772
707 37.559684999999995,55.815741
708 37.496515,55.809975
709 37.570716,55.861166000000004
710 37.54241,55.773601
711 37.518138,55.80104
712 37.500612,55.805523
713 37.535475,55.836888
714 37.504321999999995,55.821386
715 37.501609,55.855588
716 37.506783,55.794143000000005
717 37.520914000000005,55.796385
718 37.531702,55.78436899999999
719 37.568929,55.813334
720 37.529367,55.822883
721 37.503064,55.862196
722 37.555571,55.875178000000005
723 37.534191,55.86721800000001
724 37.496192,55.81823000000001
725 37.494575,55.849782
726 37.523348,55.798519999999996
727 37.481145,55.845895
728 37.535233000000005,55.800934
729 37.473267,55.85609300000001
730 37.484406,55.837803
731 37.489787,55.86608100000001
732 37.54833,55.805907
733 37.56883,55.780016
734 37.504196,55.822079
735 37.515461,55.798272
736 37.514059,55.778866
737 37.468515000000004,55.868182
738 37.562694,55.877343999999994
739 37.519306,55.874339
740 37.575298,55.812003000000004
741 37.566359000000006,55.87248100000001
742 37.539679,55.875395
743 37.527229,55.83846
744 37.49064,55.871334
745 37.516,55.822711
746 37.546057,55.778674
747 37.526016,55.839016
748 37.55982,55.782365000000006
749 37.57713,55.78915799999999
750 37.524749,55.821861
751 37.565452,55.844035999999996
752 37.485197,55.829699
753 37.56644,55.857801
754 37.561437,55.868263
755 37.560753999999996,55.808446999999994
756 37.561895,55.881636
757 37.521857000000004,55.882172
758 37.347027000000004,55.945685
759 37.552803999999995,55.870799
760 37.504861,55.817420999999996
761 37.516844,55.846851
762 37.491009000000005,55.829112
763 37.53437,55.882611
764 37.492446,55.882389
765 37.523034,55.85902900000001
766 37.574399,55.797291
767 37.573474,55.79229599999999
768 37.540003000000006,55.892309
769 37.560763,55.891451
770 37.519225,55.827661
771 37.493263,55.817502000000005
772 37.541871,55.800084
773 37.559684999999995,55.815741
774 37.570132,55.74677
775 37.541871,55.800084
776 37.484559000000004,55.680721999999996
777 37.464203000000005,55.718141
778 37.474731,55.706543
779 37.351078,55.640985
780 37.399506,55.736194
781 37.509128000000004,55.697990000000004
782 37.399964000000004,55.646584
783 37.492733,55.658149
784 37.523365999999996,55.741409
785 37.428926000000004,55.745123
786 37.371326,55.641615
787 37.422098999999996,55.724753
788 37.465802000000004,55.661983
789 37.414176,55.737030000000004
790 37.523231,55.727759
791 37.504025,55.703270999999994
792 37.498599,55.747454000000005
793 37.473698,55.725554
794 37.406531,55.657534999999996
795 37.492805,55.672757
796 37.495419,55.740446
797 37.491359,55.764425
798 37.40433,55.76231800000001
799 37.411526,55.721016000000006
800 37.500405,55.690461
801 37.507026,55.694124
802 37.461095,55.716595
803 37.495096000000004,55.737821
804 37.493533,55.684045999999995
805 37.446011999999996,55.677473
806 37.483616,55.653101
807 37.408328000000004,55.741647
808 37.495446,55.664426
809 37.412721000000005,55.757536
810 37.493075,55.744859999999996
811 37.462694,55.736259999999994
812 37.465002,55.668935
813 37.345616,55.64520699999999
814 37.410583,55.742939
815 37.469665,55.653304000000006
816 37.341206,55.6486
817 37.455983,55.71448
818 38.127178,55.920668000000006
error
819 37.504483,55.672807999999996
820 37.513556,55.709272
821 37.394,55.73885500000001
822 37.512648999999996,55.70249499999999
823 37.41467,55.764648
824 37.490003,55.696991000000004
825 37.294717999999996,55.614245999999994
error
826 37.549588,55.746607999999995
827 37.412836999999996,55.7559
828 37.456226,55.712045999999994
829 37.504762,55.672169
830 37.39744,55.708415
831 37.428827000000005,55.721548999999996
832 37.455408,55.725731999999994
833 37.423509,55.732352
834 37.405049,55.717224
835 37.457285999999996,55.670965
836 37.491611,55.664299
837 37.439706,55.731997
838 37.390603999999996,55.65773299999999
839 37.402228,55.652882999999996
840 37.36352,55.63909
841 37.417832000000004,55.750868999999994
842 37.415981,55.716174
843 37.341376000000004,55.642596
844 37.380992,55.634278
845 37.559892,55.745205000000006
846 37.347656,55.638866
847 37.413646,55.770517000000005
848 37.5183,55.738662
849 37.455543,55.666442
850 37.863369,55.737795999999996
851 37.725046999999996,55.77551
852 37.782135,55.743198
853 37.829512,55.824577000000005
854 37.786941,55.761435999999996
855 37.830203999999995,55.760282
856 37.728703,55.7956
857 37.831767,55.81652
858 37.793157,55.793723
859 37.82546,55.742732
860 37.688126000000004,55.80376700000001
error
861 37.837992,55.76209
862 37.795744,55.793743000000006
863 37.830114,55.718187
864 37.684775,55.793141000000006
865 37.783635,55.759527
866 37.827841,55.804855
867 37.711779,55.80846700000001
868 37.813243,55.753129
869 37.792753000000005,55.74746999999999
870 37.806298999999996,55.80729399999999
871 37.859013,55.742371999999996
872 37.740812,55.78978000000001
873 37.759803000000005,55.806651
874 37.874787,55.744960999999996
875 37.701879,55.812787
876 37.776745,55.824318999999996
877 37.824490000000004,55.80129300000001
878 37.827257,55.732944999999994
879 37.732386,55.781955
880 37.890831,55.710058
881 37.88704,55.710453
882 37.762668,55.7426
883 37.796562,55.790676
884 37.786266999999995,55.754993000000006
885 37.715022,55.811776
886 37.883671,55.717366000000005
887 37.814717,55.793915000000005
888 37.684765999999996,55.789775
889 37.820196,55.754856000000004
890 37.804072,55.813329
891 37.779628,55.80809300000001
892 37.738288,55.780066000000005
893 37.752975,55.827085
894 37.834821000000005,55.72398199999999
895 37.831156,55.776912
896 37.810414,55.807486
897 37.872101,55.741859999999996
898 37.685241999999995,55.793094999999994
899 37.852769,55.735439
900 37.817834000000005,55.822084
901 37.761276,55.829067
902 37.836645000000004,55.781023
903 37.815301,55.80389399999999
904 37.728586,55.813506000000004
905 37.696759,55.816293
906 37.720286,55.80939300000001
907 37.715686,55.818088
908 37.786411,55.805579
909 37.835692,55.77316999999999
910 37.797038,55.758499
911 37.706164,55.783590000000004
912 37.792519,56.003417000000006
913 37.827419,55.754405000000006
914 37.866585,55.819545
error
915 37.825451,55.768693999999996
916 37.806407,55.724357999999995
917 37.832827,55.74090699999999
918 37.778218,55.746669
919 37.826898,55.763179
920 37.863585,55.819762
error
921 37.85259,55.71338000000001
error
922 37.818265000000004,55.813061
923 37.807978999999996,55.818887
924 37.735261,55.809398
925 37.792609000000006,55.790074
926 37.864627,55.7035
927 37.80338,55.750459
928 37.784803000000004,55.79760400000001
929 37.85047,55.738987
930 37.72051,55.801708
931 37.76999,55.668686
932 37.712812,55.884115
933 37.61963,55.697163
934 37.541871,55.800084
935 37.76999,55.668686
936 37.700002000000005,55.67722
937 37.709236,55.60546
938 37.723358000000005,55.626614000000004
939 37.616593,55.718623
940 37.649103000000004,55.720966000000004
941 37.629754,55.680336
942 37.702337,55.623326
943 37.682134000000005,55.685802
944 37.625648,55.714328
945 37.663925,55.70806999999999
946 37.620968,55.60234200000001
947 37.668848,55.682559
948 37.738818,55.617154000000006
949 37.72917,55.606848
950 37.674678,55.640878
951 37.641378,55.584543000000004
952 37.719908000000004,55.624596
953 37.714806,55.606761
954 37.645276,55.577739
955 37.736967,55.60831700000001
956 37.667995,55.591126
957 37.72573,55.616163
958 37.674552,55.58497
959 37.657907,55.714414
960 37.69542,55.675966
961 37.604331,55.629684
962 37.612308,55.658439
error
963 37.680077000000004,55.577989
964 37.742187,55.634568
965 37.659973,55.642219999999995
966 37.75497,55.620337
967 37.717115,55.601746999999996
968 37.759282,55.631341000000006
969 37.681298999999996,55.67663100000001
970 37.59657,55.634852
971 37.59339,55.599641000000005
972 37.656586,55.681087
973 37.674067,55.628783999999996
974 37.613728,55.646172
error
975 37.621579,55.663918
976 37.653748,55.668634999999995
977 37.697478000000004,55.639801
978 37.675271,55.629816000000005
979 37.746588,55.61960500000001
980 37.659182,55.646471999999996
981 37.601268,55.617805000000004
982 37.595968,55.609701
983 37.631775,55.676605
984 37.626645,55.65999300000001
985 37.724022999999995,55.616275
986 37.671929,55.679269999999995
987 37.687515000000005,55.68091999999999
988 37.655634,55.70473199999999
989 37.655615999999995,55.600501
990 37.734084,55.623697
991 37.619701,55.708912
992 37.585368,55.616778000000004
993 37.614832,55.71756800000001
994 37.595159,55.587687
995 37.619818,55.60566800000001
996 37.606721,55.627976000000004
997 37.60823,55.636285
998 37.755715,55.636956000000005
999 37.653397,55.62111899999999
1000 37.663368,55.624179000000005
1001 37.733581,55.62301
1002 37.588646999999995,55.609665
1003 37.665488,55.633414
1004 37.605247,55.591421
1005 37.58579,55.599265
1006 37.65143,55.581927
1007 37.688782,55.581062
1008 37.701134,55.619243999999995
1009 37.753847,55.608810999999996
1010 37.774184999999996,55.63949100000001
1011 37.745825,55.602927
1012 37.766163,55.639933
1013 37.681227,55.684143000000006
1014 37.594297,55.616864
1015 37.609146,55.638180000000006
1016 37.614428000000004,55.624896
1017 37.743049,55.635406
1018 37.640075,55.59163
1019 37.608302,55.652075
error
1020 37.717869,55.636199
1021 37.617653000000004,55.59954499999999
1022 37.60735,55.718953000000006
1023 37.6698,55.784364000000004
1024 37.60735,55.718953000000006
1025 37.487029,55.630878
1026 37.555139000000004,55.702901000000004
1027 37.532771000000004,55.544748999999996
1028 37.556442,55.69798
1029 37.566952,55.67261
1030 37.57484,55.706741
1031 37.543587,55.601828000000005
1032 37.526816,55.685056
1033 37.612973,55.641656000000005
1034 37.519242999999996,55.540501
1035 37.579744,55.690654
1036 37.532331,55.636361
1037 37.593003,55.69195799999999
1038 37.507295,55.63086800000001
1039 37.491988,55.622466
1040 37.539320000000004,55.680087
1041 37.559703000000006,55.65209
1042 37.522521999999995,55.628972
1043 37.603918,55.663639
error
1044 37.579008,55.675017000000004
1045 37.524013000000004,55.68326
1046 37.525001,55.663563
1047 37.511984000000005,55.657362
1048 37.516647,55.63764200000001
1049 37.580256,55.656936
1050 37.590057,55.671564000000004
1051 37.525001,55.64984499999999
1052 37.532277,55.662887
1053 37.528774,55.532481999999995
1054 37.582358,55.565763000000004
1055 37.534101,55.669194
1056 37.558265999999996,55.699070999999996
1057 37.502813,55.533522
1058 37.578981,55.50338000000001
1059 37.574327000000004,55.685092000000004
1060 37.548905,55.695073
1061 37.554376,55.676843999999996
1062 37.5334,55.614953
1063 37.563844,55.548136
1064 37.570608,55.565376
1065 37.559038,55.662293000000005
1066 37.528334,55.635177
1067 37.588575,55.65656
error
1068 37.577822,55.54053100000001
1069 37.475701,55.640467
1070 37.559047,55.664634
1071 37.495284999999996,55.534591000000006
1072 37.549633,55.666929
1073 37.53402,55.643443999999995
1074 37.482214,55.644089
1075 37.581595,55.567306
1076 37.54038,55.592082999999995
1077 37.529654,55.686980000000005
1078 37.549085,55.66995
1079 37.571579,55.69008
1080 37.595483,55.643866
error
1081 37.577139,55.652283
1082 37.553756,55.5448
1083 37.477606,55.633297
1084 37.552436,55.606065
1085 37.56892,55.551726
1086 37.575603,55.5636
1087 37.523249,55.67126999999999
1088 37.50778,55.540491
1089 37.586696999999994,55.573948
1090 37.544494,55.612593999999994
1091 37.547189,55.54946999999999
1092 37.584685,55.570471999999995
1093 37.530076,55.549322
1094 37.498653000000004,55.64605
1095 37.517491,55.605536
1096 37.563753999999996,55.669315000000005
1097 37.517266,55.59794300000001
1098 37.595034000000005,55.65434499999999
error
1099 37.576133,55.684884
1100 37.559684999999995,55.815741
1101 37.671624,55.723268000000004
1102 37.76999,55.668686
1103 37.60735,55.718953000000006
1104 37.720043,55.732352
1105 37.716432,55.733827000000005
1106 37.767590999999996,55.672001
1107 37.741288,55.738966000000005
1108 37.71998,55.690872
1109 37.720321999999996,55.740669
1110 37.719334,55.74466700000001
1111 37.823745,55.707588
1112 37.753146,55.713253
1113 37.751314,55.665203000000005
1114 37.847829,55.683893999999995
1115 37.776269,55.660541
1116 37.764276,55.672529000000004
1117 37.735566,55.651516
1118 37.920808,55.684346
1119 37.748681,55.647670999999995
1120 37.771211,55.707765
1121 37.92644,55.682503000000004
1122 37.769666,55.672062
1123 37.766738000000004,55.711583999999995
1124 37.748996000000005,55.716345999999994
1125 37.855383,55.680488000000004
1126 37.777967,55.666853
1127 37.792411,55.714389000000004
1128 37.682862,55.70020699999999
1129 37.744648,55.700775
1130 37.75691,55.704443000000005
1131 37.777104,55.661928
1132 37.84534,55.701501
1133 37.752176,55.714653000000006
1134 37.67917,55.751852
1135 37.799876,55.695012
1136 37.764654,55.660140000000006
1137 37.814878,55.700796
1138 37.722487,55.649048
1139 37.923116,55.688120999999995
1140 37.689778999999994,55.757364
1141 37.727022999999996,55.680265000000006
1142 37.760045,55.652283
1143 37.774813,55.700547
1144 37.820062,55.697681
1145 37.792941,55.718689000000005
1146 37.741351,55.674966000000005
1147 37.701017,55.757394
1148 37.742124,55.70601
1149 37.771463,55.678895
1150 37.740938,55.701227
1151 37.719648,55.683944999999994
1152 37.729359,55.688
1153 37.770034,55.728368
1154 37.757278,55.710854000000005
1155 37.809686,55.674255
1156 37.742276000000004,55.67825
1157 37.75108,55.686335
1158 37.737623,55.643917
1159 37.767492,55.663679
1160 37.851242,55.691542000000005
1161 37.774516999999996,55.665259
1162 37.745933,55.668787
1163 37.860019,55.682590000000005
1164 37.849759999999996,55.689161999999996
1165 37.862282,55.686462
1166 37.76981,55.655427
1167 37.776412,55.66372
1168 37.797334,55.63158000000001
1169 37.918544,55.701668000000005
1170 37.919002,55.705721
1171 37.736896,55.71749200000001
1172 37.789393,55.667933999999995
1173 37.718732,55.643287
1174 37.751987,55.67926
1175 37.721265,55.66800500000001
1176 37.938181,55.703718
1177 37.652445,55.883772
1178 37.712812,55.884115
1179 37.683265999999996,55.884822
1180 37.618272999999995,55.877602
1181 37.626653999999995,55.817537
1182 37.670483000000004,55.872193
1183 37.592923,55.85949399999999
1184 37.691064000000004,55.879884
1185 37.642887,55.874511
1186 37.72034,55.880883999999995
1187 37.608895000000004,55.86056
1188 37.642644,55.867465
1189 37.6524,55.87866700000001
1190 37.719172,55.874258
1191 37.570159000000004,55.894636
1192 37.592087,55.827808
1193 37.654089,55.868162
1194 37.616207,55.895615
1195 37.662794,55.818006999999994
1196 37.6509,55.889759999999995
1197 37.635107,55.80364599999999
1198 37.633068,55.803702
1199 37.641836,55.810324
1200 37.613485,55.864439000000004
1201 37.642185999999995,55.852799
1202 37.552957,55.906622999999996
1203 37.645025,55.803175
1204 37.610206,55.889649
1205 37.594405,55.883989
1206 37.691646999999996,55.87279399999999
1207 37.617545,55.896846999999994
1208 37.624525,55.824728
1209 37.621615000000006,55.893172
1210 37.601690000000005,55.890977
1211 37.668426000000004,55.859266000000005
1212 37.653128,55.827403000000004
1213 37.693471,55.884232
1214 37.635377000000005,55.867051000000004
1215 37.660727,55.835432
1216 37.64348,55.833602
1217 37.575055,55.824631999999994
1218 37.601366999999996,55.86653100000001
1219 37.611958,55.795908999999995
1220 37.696364,55.873799
1221 37.621094,55.817632999999994
1222 37.641602,55.874264000000004
1223 37.690426,55.852789
1224 37.643381,55.825027
1225 37.581388000000004,55.89344499999999
1226 37.584002000000005,55.903448
1227 37.670932,55.88928000000001
1228 37.580014,55.875455
1229 37.556307000000004,55.935113
1230 37.589822999999996,55.896251
1231 37.578029,55.900567
1232 37.542671,55.926993
1233 37.470913,55.80438
1234 37.430139000000004,55.846740000000004
1235 37.46458,55.781185
1236 37.48384,55.780502
1237 37.360457000000004,55.83025500000001
1238 37.446883,55.857457
1239 37.476339,55.786570999999995
1240 37.34682,55.856492
1241 37.453163,55.785078000000006
1242 37.412496000000004,55.807390000000005
1243 37.482214,55.772845999999994
1244 37.381405,55.839749
1245 37.359513,55.836059
1246 37.456037,55.846557999999995
1247 37.40486,55.797063
1248 37.457124,55.800194999999995
1249 37.41935,55.806616000000005
1250 37.465711999999996,55.786354
1251 37.382393,55.900128
1252 37.3523,55.846249
1253 37.361211,55.841397
1254 37.42686,55.856694
1255 37.47156,55.769524
1256 37.448662,55.833814000000004
1257 37.382825,55.903973
1258 37.349183000000004,55.842378000000004
1259 37.410088,55.858589
1260 37.487164,55.799133
1261 37.40274,55.80731899999999
1262 37.348482000000004,55.85500699999999
1263 37.444736,55.828576
1264 37.477336,55.780460999999995
1265 37.398464000000004,55.81578199999999
1266 37.394771999999996,55.810283999999996
1267 37.394934,55.802305000000004
1268 37.413321999999994,55.855047
1269 37.459352,55.833091
1270 37.431521999999994,55.860438
1271 37.452327000000004,55.807117000000005
1272 37.444018,55.848316000000004
1273 37.400791,55.79681
1274 37.364939,55.847503
1275 37.357762,55.850832999999994
1276 37.463143,55.803074
1277 37.392247999999995,55.886963
1278 37.397575,55.894515000000006
1279 37.231566,55.998000000000005
error
1280 37.190765,55.976302000000004
error
1281 37.153690999999995,55.979903
error
1282 37.191142,55.967808999999995
error
1283 37.192246999999995,55.98500500000001
error
1284 37.155614,55.987442
error
1285 37.164956,55.9822
error
1286 37.202524,55.995281999999996
error
1287 37.220921000000004,55.997985
error
1288 37.207653,55.993877000000005
error
1289 37.192687,55.993248
error
1290 37.186138,55.984556999999995
error
1291 37.211821,56.005848
error
1292 37.186138,55.997149
error
1293 37.228314000000005,55.99153100000001
error
1294 37.153323,55.975531000000004
error
1295 37.176518,55.987352
error
1296 37.153682,55.98100600000001
error
1297 37.422674,55.557536
1298 37.332097,55.505545999999995
1299 37.309208,55.622039
1300 37.160788000000004,55.597820999999996
1301 37.346802000000004,55.59550600000001
1302 37.480669,55.571957999999995
error
1303 37.541,55.469255000000004
1304 37.360295,55.600776
1305 37.57006,55.511770999999996
1306 37.531118,55.514773
1307 37.576133,55.684884
1308 37.299695,55.490136
1309 36.870983,55.430222
error
1310 37.181656,55.313926
1311 37.268244,55.42635
1312 37.300799,55.474362
error
1313 37.517472999999995,55.934180000000005
1314 37.739186,55.358693
error
1315 38.127402000000004,55.60020600000001
1316 38.776525,55.074592


objects.to_csv(r'objects.csv')


X = objects[["Latitude","Longitude"]]
max_k = 100

# iterations
distortions = [] 
for i in range(1, max_k+1):
    if len(X) >= i:
       model = cluster.KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
       model.fit(X)
       distortions.append(model.inertia_)
# best k: the lowest derivative
k = [i*100 for i in np.diff(distortions,2)].index(min([i*100 for i in np.diff(distortions,2)]))

# plot
fig, ax = plt.subplots()
ax.plot(range(1, len(distortions)+1), distortions)
ax.axvline(k, ls='--', color="red", label="k = "+str(k))
ax.set(title='The Elbow Method', xlabel='Number of clusters', ylabel="Distortion")
ax.legend()
ax.grid(True)
plt.show()


k = 6
model = cluster.KMeans(n_clusters=k, init='k-means++')
X = objects[["Latitude","Longitude"]]

# clustering
objects_X = X.copy()
objects_X["cluster"] = model.fit_predict(X)

# find real centroids
closest, distances = scipy.cluster.vq.vq(model.cluster_centers_, 
                     objects_X.drop("cluster", axis=1).values)
objects_X["centroids"] = 0
for i in closest:
    objects_X["centroids"].iloc[i] = 1

# add clustering info to the original dataset
objects[["cluster","centroids"]] = objects_X[["cluster","centroids"]]
objects.sample(5)

/Users/maxim/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  iloc._setitem_with_indexer(indexer, value)


fig, ax = plt.subplots()

fig = plt.gcf()

# Change seaborn plot size
fig.set_size_inches(20, 8)

sns.scatterplot(x="Latitude", y="Longitude", data=objects, 
                palette=sns.color_palette("bright",k),
                hue='cluster', size="centroids", size_order=[1,0],
                legend="brief", ax=ax).set_title('Clustering (k='+str(k)+')')
th_centroids = model.cluster_centers_
ax.scatter(th_centroids[:,0], th_centroids[:,1], s=50, c='black', marker="x")

<matplotlib.collections.PathCollection at 0x13232e520>


model_affprop = cluster.AffinityPropagation()


k = objects["cluster"].nunique()

fig = plt.gcf()

# Change seaborn plot size
fig.set_size_inches(20, 8)

sns.scatterplot(x="Latitude", y="Longitude", data=objects, 
                palette=sns.color_palette("bright",k),
                hue='cluster', size="centroids", size_order=[1,0],
                legend="brief").set_title('Clustering (k='+str(k)+')')

Text(0.5, 1.0, 'Clustering (k=6)')


X = objects[["Latitude","Longitude"]]
map_shape = (5,5)
## scale data
scaler = preprocessing.StandardScaler()
X_preprocessed = scaler.fit_transform(X.values)
## clustering
model_som = MiniSom(x=map_shape[0], y=map_shape[1], input_len=X.shape[1])
model_som.train_batch(X_preprocessed, num_iteration=100, verbose=False)
## build output dataframe
objects_X = X.copy()
objects_X["cluster"] = np.ravel_multi_index(np.array([model_som.winner(x) for x in X_preprocessed]).T, dims=map_shape)

cluster_centers = np.array([vec for center in model_som.get_weights() for vec in center])
## find real centroids
cluster_centers = np.array([vec for center in model_som.get_weights() for vec in center])
closest, distances = scipy.cluster.vq.vq(model.cluster_centers_, X_preprocessed)
objects_X["centroids"] = 0
for i in closest: objects_X["centroids"].iloc[i] = 1

## add clustering info to the original dataset
objects[["cluster","centroids"]] = objects_X[["cluster","centroids"]]
## plot
k = objects["cluster"].nunique()
fig, ax = plt.subplots()

fig = plt.gcf()

# Change seaborn plot size
fig.set_size_inches(20, 8)

sns.scatterplot(x="Latitude", y="Longitude", data=objects, 
                palette=sns.color_palette("bright",k),
                hue='cluster', size="centroids", size_order=[1,0],
                legend="brief", ax=ax).set_title('Clustering (k='+str(k)+')')
th_centroids = scaler.inverse_transform(cluster_centers)
ax.scatter(th_centroids[:,0], th_centroids[:,1], s=50, c='black', marker="x")

/Users/maxim/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  iloc._setitem_with_indexer(indexer, value)

<matplotlib.collections.PathCollection at 0x1349252e0>


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude


x, y = "Latitude", "Longitude"
color = "cluster"
popup = "District"
marker = "centroids"
data = objects.copy()

# create color column
lst_elements = sorted(list(objects[color].unique()))
lst_colors = ['#%06X' % np.random.randint(0, 0xFFFFFF) for i in range(len(lst_elements))]
data["color"] = data[color].apply(lambda x: lst_colors[lst_elements.index(x)])

# create size column (scaled)
scaler = preprocessing.MinMaxScaler(feature_range=(3,15))

# initialize the map with the starting location
map_ = folium.Map(location=[latitude, longitude], tiles="cartodbpositron", zoom_start=11)

# add points
data.apply(lambda row: folium.CircleMarker(
           location=[row[x],row[y]], popup=row[popup],
           color=row["color"], fill=True).add_to(map_), axis=1)

# add html legend
legend_html = """<div style="position:fixed; bottom:10px; left:10px; border:2px solid black; z-index:9999; font-size:12px;">&nbsp;<b>"""+color+""":</b><br>"""
for i in lst_elements:
     legend_html = legend_html+"""&nbsp;<i class="fa fa-circle 
     fa-1x" style="color:"""+lst_colors[lst_elements.index(i)]+"""">
     </i>&nbsp;"""+str(i)+"""<br>"""
legend_html = legend_html+"""</div>"""
map_.get_root().html.add_child(folium.Element(legend_html))
map_


for index in range(0,25):
    print(objects.loc[objects['cluster'] == index].shape)

(90, 10)
(83, 10)
(69, 10)
(57, 10)
(79, 10)
(119, 10)
(22, 10)
(14, 10)
(79, 10)
(69, 10)
(31, 10)
(29, 10)
(21, 10)
(36, 10)
(16, 10)
(114, 10)
(67, 10)
(67, 10)
(13, 10)
(55, 10)
(67, 10)
(49, 10)
(18, 10)
(36, 10)
(17, 10)


cluster5 = objects.loc[objects['cluster'] == 5]
cluster5.to_csv(r'cluster5.csv')


print('There are {} uniques categories.'.format(len(cluster5['Category'].unique())))
print(cluster5['Category'].unique())

There are 6 uniques categories.
['Pools' 'AdultHospital' 'AdultDentalClinic' 'ChildrenDentalClinic'
 'ChildrenHospital' 'School']


cluster5['AdmArea'].unique()

array(['Юго-Восточный административный округ',
       'Южный административный округ', 'Курьяновский парк',
       'исторический район Волхонка-ЗИЛ',
       'Юго-Западный административный округ'], dtype=object)


address = 'Moscow, RU'

geolocator = Nominatim(user_agent="moscow_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude

# create map of Moscow using latitude and longitude values
map_moscow = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, name in zip(cluster5['Latitude'], cluster5['Longitude'], cluster5['ObjectName']):
    label = '{}'.format(name)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_moscow)  
    
map_moscow

	global_id	ObjectName	NameWinter	PhotoWinter	AdmArea	District	Address	Email	WebSite	HelpPhone	...	DimensionsWinter	Lighting	SurfaceTypeWinter	Paid	PaidComments	DisabilityFriendly	ServicesWinter	geodata_center	geoarea
0	282897592	Физкультурно-оздоровительный комплекс с бассей...	бассейн плавательный 25-ти метровый крытый	Photo:126e8a14-8169-4b05-b424-51b812da6a5e\n\n	Северо-Восточный административный округ	Алтуфьевский район	Инженерная улица, дом 5, корпус 1	s82@mossport.ru	s82.mossport.ru	(499) 900-90-18	...	Square:350\nLength:25\nWidth:14\nDepth:1.8\n\n	смешанное освещение	специальное покрытие	платно	NaN	частично приспособлен	общая физическая подготовка	01010000003C66FB9505CA4240CCB560568EF04B40	NaN
1	407157820	Фитнес клуб «Ворлд Класс»	бассейн плавательный крытый	Photo:c173e2b2-2d62-4017-9d66-003c6c291985\n\n	Центральный административный округ	район Якиманка	Житная улица, дом 14, строение 1	NaN	www.worldclass.ru/clubs/zhitnaya/detail	(495) 771-66-00	...	Square:60\nLength:15\nWidth:4\nDepth:\n\n	освещение газоразрядными лампами	специальное покрытие	платно	NaN	NaN	общая физическая подготовка	0101000000479DC225D0CE4240A666ECCDA2DD4B40	NaN
2	407158063	Спортивный комплекс высшего учебного заведения...	бассейн плавательный 50-ти метровый крытый	Photo:73775081-f3f9-4286-9e78-b296d8914ab2\n\n	Центральный административный округ	Басманный район	Госпитальная набережная, дом 4, строение 2	NaN	bmstu.ru	(499) 263-22-37	...	Square:1250\nLength:50\nWidth:25\nDepth:\n\n	освещение газоразрядными лампами	специальное покрытие	платно	NaN	частично приспособлен	общая физическая подготовка	01010000004E336D2150D94240AF0469CAD7E24B40	NaN
3	407173850	Спортивный комплекс высшего учебного заведения...	бассейн плавательный 25-ти метровый крытый	Photo:3f8a082f-f943-4758-88e7-d296bceb5393\n\n	Северо-Восточный административный округ	район Марьина Роща	Новосущёвская улица, дом 24, строение 1	tu@miit.ru	www.miit.ru	(495) 684-40-25	...	Square:350\nLength:25\nWidth:14\nDepth:\n\n	освещение лампами накаливания	специальное покрытие	платно	NaN	частично приспособлен	общая физическая подготовка	0101000000312ECF7B63CD424013FB5FA123E54B40	NaN
4	407193269	Фитнес клуб «Ворлд Класс»	бассейн плавательный 25-ти метровый крытый	Photo:f880b17d-a87f-4a5a-ac4e-41be68d851b0\n\n	Южный административный округ	Донской район	Варшавское шоссе, дом 12А	NaN	www.worldclass.ru/clubs/tulskaya/detail/	(495) 633-03-03	...	Square:125\nLength:25\nWidth:5\nDepth:\n\n	смешанное освещение	специальное покрытие	платно	NaN	не приспособлен	общая физическая подготовка	0101000000C12C000716CF4240FEF9164C3AD94B40	NaN

	ObjectName	AdmArea	District	Address
0	Физкультурно-оздоровительный комплекс с бассей...	Северо-Восточный административный округ	Алтуфьевский район	Инженерная улица, дом 5, корпус 1
1	Фитнес клуб «Ворлд Класс»	Центральный административный округ	район Якиманка	Житная улица, дом 14, строение 1
2	Спортивный комплекс высшего учебного заведения...	Центральный административный округ	Басманный район	Госпитальная набережная, дом 4, строение 2
3	Спортивный комплекс высшего учебного заведения...	Северо-Восточный административный округ	район Марьина Роща	Новосущёвская улица, дом 24, строение 1
4	Фитнес клуб «Ворлд Класс»	Южный административный округ	Донской район	Варшавское шоссе, дом 12А

	ObjectName	AdmArea	District	Address	Category	Longitude	Latitude
0	Физкультурно-оздоровительный комплекс с бассей...	Северо-Восточный административный округ	Алтуфьевский район	Инженерная улица, дом 5, корпус 1	Pools	37.578298	55.879303
1	Фитнес клуб «Ворлд Класс»	Центральный административный округ	район Якиманка	Житная улица, дом 14, строение 1	Pools	37.615632	55.730978
2	Спортивный комплекс высшего учебного заведения...	Центральный административный округ	Басманный район	Госпитальная набережная, дом 4, строение 2	Pools	37.696642	55.771753
3	Спортивный комплекс высшего учебного заведения...	Северо-Восточный административный округ	район Марьина Роща	Новосущёвская улица, дом 24, строение 1	Pools	37.604610	55.790140
4	Фитнес клуб «Ворлд Класс»	Южный административный округ	Донской район	Варшавское шоссе, дом 12А	Pools	37.617860	55.697138
5	Фитнес-клуб «Пирамида»	Юго-Западный административный округ	Академический район	улица Дмитрия Ульянова, дом 31	Pools	37.585440	55.684726
6	Физкультурно-оздоровительный комплекс с бассей...	Северо-Восточный административный округ	район Лианозово	Абрамцевская улица, дом 15, корпус 1	Pools	37.571525	55.906749
7	Спортивный комплекс «Тушино»	Северо-Западный административный округ	район Северное Тушино	улица Вилиса Лациса, домовладение 8	Pools	37.427094	55.865384
8	Спортивный комплекс «Аквамарин»	Северо-Западный административный округ	район Митино	улица Рословка, домовладение 5	Pools	37.366583	55.831433
9	Спортивный комплекс «Косино»	Восточный административный округ	район Косино-Ухтомский	улица Николая Старостина, дом 8А	Pools	37.853093	55.729731

	array\|ChiefPosition	array\|ReopenDate	array\|ClarificationWorkingHours	array\|Category	array\|Email	array\|PaidServiceInfo	array\|ShortName	array\|CloseFlag	array\|CloseDate	array\|Specialization	...	array\|WorkingHours	array\|OrgInfo	array\|FullName	array\|ChiefGender	array\|Fax	array\|Extrainfo	array\|ChiefName	array\|ObjectAddress	array\|geodata_center	array\|DrugStore
0	главный врач	None	None	Больница взрослая	{'Email': 'gkb24@zdrav.mos.ru'}	оказываются	ГБУЗ ГКБ № 24 ДЗМ	действует	None	многопрофильное медицинское учреждение	...	[{'DayWeek': 'понедельник', 'WorkHours': 'круг...	{'OGRN': '1037739726771', 'ChiefPosition': 'гл...	Государственное бюджетное учреждение здравоохр...	мужской	{'Fax': '(495) 613-04-34'}	None	Родоман Григорий Владимирович	[{'Availability': {'available_o': 'частично', ...	NaN	NaN
1	заведующий филиалом	None	None	Больница взрослая	{'Email': 'gb17@zdrav.mos.ru'}	отсутствуют	ГБУЗ ГКБ № 17 ДЗМ Филиал «Внуковский»	действует	None	многопрофильное медицинское учреждение	...	[{'DayWeek': 'понедельник', 'WorkHours': '08:0...	{'OGRN': '1027739805114', 'ChiefPosition': 'гл...	Государственное бюджетное учреждение здравоохр...	женский	{'Fax': '(495) 736-62-34'}	None	Словеснова Ираида Викторовна	{'Availability': {'available_o': 'частично', '...	{'type': 'geometry', 'value': '01010000001FA88...	NaN
2	главный врач	None	None	Больница взрослая	{'Email': 'gkb31@zdrav.mos.ru'}	оказываются	ГБУЗ ГКБ № 31 ДЗМ	действует	None	многопрофильное медицинское учреждение	...	[{'DayWeek': 'понедельник', 'WorkHours': 'круг...	{'OGRN': '1037700216069', 'ChiefPosition': 'гл...	Государственное бюджетное учреждение здравоохр...	женский	{'Fax': '(499) 432-08-15'}	None	Ефремова Наталья Михайловна	[{'Availability': {'available_o': 'частично', ...	{'type': 'geometry', 'value': '0101000000590D9...	NaN
3	заведующий филиалом	None	None	Больница взрослая	{'Email': 'gkbb@zdrav.mos.ru'}	оказываются	ГБУЗ ГКБ им. С. П. Боткина филиал № 1 ДЗМ офта...	действует	None	офтальмология	...	[{'DayWeek': 'понедельник', 'WorkHours': '08:0...	{'OGRN': '1037739085900', 'ChiefPosition': 'гл...	Государственное бюджетное учреждение здравоохр...	женский	{'Fax': '(495) 699-01-31'}	None	Аржиматова Гульжияна Шевкетовна	[{'Availability': {'available_o': 'полностью',...	NaN	NaN
4	главный врач	None	None	Больница взрослая	{'Email': 'vgb@zdrav.mos.ru'}	оказываются	ГБУЗ Вороновская больница ДЗМ	действует	None	многопрофильное медицинское учреждение	...	[{'DayWeek': 'понедельник', 'WorkHours': 'круг...	{'OGRN': '1035011457612', 'ChiefPosition': 'гл...	Государственное бюджетное учреждение здравоохр...	мужской	{'Fax': '(495) 850-71-75'}	None	Гурин Александр Анатольевич	{'Availability': {'available_o': 'частично', '...	{'type': 'geometry', 'value': '0101000000527B5...	NaN

	array\|ChiefPosition	array\|ReopenDate	array\|ClarificationWorkingHours	array\|AmbulanceStation	array\|Category	array\|Email	array\|PaidServiceInfo	array\|ShortName	array\|CloseFlag	array\|CloseDate	...	array\|OrgInfo	array\|DrugStore	array\|DoctorsSpecialtiesListForSelfRecording	array\|FullName	array\|ChiefGender	array\|Fax	array\|Extrainfo	array\|ChiefName	array\|ObjectAddress	array\|geodata_center
0	главный врач	None	None	нет	Стоматологическая поликлиника взрослая	{'Email': 'sp7@zdrav.mos.ru'}	оказываются	ГАУЗ СП № 7 ДЗМ	действует	None	...	{'OGRN': '1037736032069', 'ChiefPosition': 'гл...	нет	[Врач-стоматолог-терапевт, Врач-стоматолог-хир...	Государственное автономное учреждение здравоох...	мужской	{'Fax': '(499) 137-63-29'}	Проезд: м. «Ленинский проспект», две остановки...	Вартанов Олег Игоревич	{'Availability': {'available_o': 'частично', '...	NaN
1	главный врач	None	None	нет	Стоматологическая поликлиника взрослая	{'Email': 'sp24@zdrav.mos.ru'}	отсутствуют	ГАУЗ СП № 24 ДЗМ. Ортопедическое отделение	действует	None	...	{'OGRN': '1037726012686', 'ChiefPosition': 'гл...	нет	[Врач-стоматолог-терапевт, Врач-стоматолог-хир...	Государственное автономное учреждение здравоох...	мужской	{'Fax': '(499) 610-73-52'}	None	Гринев Андрей Валентинович	{'Availability': {'available_o': 'частично', '...	NaN
2	главный врач	None	None	нет	Стоматологическая поликлиника взрослая	{'Email': 'info@wgb.mosgorzdrav.ru'}	оказываются	ГБУЗ ЩГБ ДЗМ. Стоматологическая поликлиника	действует	None	...	{'OGRN': '1035011454400', 'ChiefPosition': 'гл...	нет	[Врач-стоматолог-терапевт, Врач-стоматолог-хир...	Государственное бюджетное учреждение здравоохр...	женский	{'Fax': '(495) 867-02-79'}	None	Лисицина Елена Вадимовна	{'Availability': {'available_o': 'частично', '...	NaN
3	главный врач	None	None	нет	Стоматологическая поликлиника взрослая	{'Email': 'SP19@zdrav.mos.ru'}	оказываются	ГАУЗ СП № 19 ДЗМ	действует	None	...	{'OGRN': '1027700299274', 'ChiefPosition': 'гл...	нет	[Врач-стоматолог-терапевт, Врач-стоматолог-хир...	Государственное автономное учреждение здравоох...	женский	{'Fax': '(499) 246-16-72'}	Проезд: м. «Фрунзенская».\nОт станции метро &q...	Остроухова Алла Александровна	{'Availability': {'available_o': 'полностью', ...	NaN
4	исполняющий обязанности главного врача	None	None	нет	Стоматологическая поликлиника взрослая	{'Email': 'sp3@zdrav.mos.ru'}	оказываются	ГАУЗ СП № 3 ДЗМ	действует	None	...	{'OGRN': '1037700123042', 'ChiefPosition': 'ис...	нет	[Врач-стоматолог-терапевт, Врач-стоматолог-хир...	Государственное автономное учреждение здравоох...	женский	{'Fax': '(499) 236-52-93'}	Проезд:ст.м.Серпуховская,с выходом к экономиче...	Могучева Людмила Викторовна	{'Availability': {'available_o': 'частично', '...	NaN

A research on identifying the best district in Moscow to buy a flat.¶

Baseline: The flat is intended for a family with children¶

Criteria:¶

1. Data preparation¶

1.1 Preparation of data on objects - swimming pools¶

Count missing values in each column¶

Getting the geographical coordinates for each object¶

1.2 Preparation of data on objects - hospitals for adults¶

1.3 Preparation of data on objects - Dental clinic for adults¶

1.4 Preparation of data on objects - Dental clinic for children¶

1.5 Preparation of data on objects - hospitals for children¶

1.6 Preparation of data on objects - School¶

1.7 Get information about the district through geocoding¶

1.8 Object clustering¶

A fundamental step for any unsupervised algorithm is to determine the optimal number of clusters into which the data may be clustered. The Elbow Method is one of the most popular methods to determine this optimal value of k.¶

k-means clustering¶

The most filled cluster is number 5. Let's look at its occupancy by category¶

Let's find out how many unique categories there are in the cluster¶

We can see that the cluster has all categories of objects¶

Let's find out what unique administrative areas Moscow has in the cluster¶

We can see that the cluster includes the following areas:¶

Finally, let's visualize the resulting clusters¶

Result¶

As a result of the research, we found out that the optimal districts to live in Moscow for a family with children are:¶

	ObjectName	AdmArea	District	Address	Category
0	ГБУЗ ДСП № 46 ДЗМ	None	None	город Москва, Новогиреевская улица, дом 17	ChildrenDentalClinic
1	ГБУЗ ДСП № 6 ДЗМ	None	None	город Москва, Марьинский бульвар, дом 10, корп...	ChildrenDentalClinic
2	ГБУЗ ДСП № 54 ДЗМ	None	None	город Москва, улица Удальцова, дом 4	ChildrenDentalClinic
3	ГАУЗ СП № 66 ДЗМ. Детское отделение	None	None	город Москва, Сосинский проезд, дом 8	ChildrenDentalClinic
4	ГБУЗ ДСП № 26 ДЗМ. Отделение ортодонтии	None	None	город Москва, улица Маршала Новикова, дом 4, к...	ChildrenDentalClinic

	ObjectName	AdmArea	District	Address	Category
0	ГБУЗ Морозовская ДГКБ ДЗМ	None	None	город Москва, 4-й Добрынинский переулок, дом 1...	ChildrenHospital
1	ГБУЗ ГКБ № 67 им. Л. А. Ворохобова ДЗМ. Детско...	None	None	город Москва, улица Саляма Адиля, дом 2/44	ChildrenHospital
2	ГБУЗ ГКБ им. Е.О. Мухина ДЗМ. Детское отделение	None	None	Российская Федерация, город Москва, внутригоро...	ChildrenHospital
3	ГБУЗ Больница «Кузнечики» ДЗМ. Детское отделение	None	None	город Москва, поселение Щаповское, село Озноби...	ChildrenHospital
4	ГБУЗ ДГКБ № 9 им. Г. Н. Сперанского ДЗМ	None	None	город Москва, Шмитовский проезд, дом 29, строе...	ChildrenHospital

	name	adress	lattitude	longitude
0	Школа № 1239	Вспольный переулок, дом 6, строение 3	55.760292	37.588808
1	Школа № 1231 имени В.Д. Поленова	Спасопесковский переулок, дом 6, строение 7	55.749866	37.589033
2	Школа № 1277 с углубленным изучением немецкого...	Сущевская ул., д. 32	55.783848	37.602067
3	Пятьдесят седьмая школа	Малый Знаменский переулок, 7/10 стр. 5	55.748615	37.604987
4	Школа № 1535	ул. Усачева, д.50	55.725803	37.564931

	ObjectName	Address	Category	Longitude	Latitude
0	Школа № 1239	Вспольный переулок, дом 6, строение 3	School	37.588808	55.760292
1	Школа № 1231 имени В.Д. Поленова	Спасопесковский переулок, дом 6, строение 7	School	37.589033	55.749866
2	Школа № 1277 с углубленным изучением немецкого...	Сущевская ул., д. 32	School	37.602067	55.783848
3	Пятьдесят седьмая школа	Малый Знаменский переулок, 7/10 стр. 5	School	37.604987	55.748615
4	Школа № 1535	ул. Усачева, д.50	School	37.564931	55.725803

	index	ObjectName	AdmArea	District	Address	Category	Longitude	Latitude	cluster
1132	494	Школа № 1359 имени авиаконструктора М.Л. Миля	Юго-Восточный административный округ	район Выхино-Жулебино	Пронская улица, дом 4, корпус 1	School	37.845340	55.701501	2
426	426	Фитнес клуб «Вандерфит фитнес»	Западный административный округ	район Кунцево	Рублёвское шоссе, дом 101, корпус 3	Pools	37.421497	55.741490	0
363	363	Спортивный комплекс дошкольного образовательно...	Юго-Восточный административный округ	район Выхино-Жулебино	улица Авиаконструктора Миля, дом 13, корпус 2	Pools	37.858204	55.681965	2
1087	449	Школа № 1311	Юго-Западный административный округ	Обручевский район	Ленинский проспект, д. 97, к. 1	School	37.523249	55.671270	5
877	239	Школа самоопределения № 734 имени А.Н. Тубельс...	Восточный административный округ	район Восточное Измайлово	Сиреневый бульвар, дом 58А	School	37.824490	55.801293	2